درخت حوزه‌های تخصصی

In recent years, ecological metaphors have gained popularity in the field of innovation. Such excessive preoccupation of researchers with common ecological metaphors, such as ecosystems, and a lack of due attention to the development of other ecological principles, challenges the systematic conceptual framework of the field. This raises the metaphorical concept of a "boundary object," which could hinder the connection between theoretical discussions on innovation ecosystems and their practical applications and policy development. Therefore, there is a need for an evidence-based approach to define and map the innovation ecosystem metaphorically. The concepts deployed would form a part of the conceptual network of the innovation ecosystem. As part of an effort to implement evidence-based practice in the field of innovation ecosystems, this study employs Scaringella's and Radziwon's (2019) systematic review framework and the comparative-qualitative content analysis method. The analysis is based on a structured matrix that is rooted in metaphor mapping. Upon analyzing the content of the selected articles, the ecological metaphors grounded in innovation ecosystems can be classified into two general categories: metaphors related to actors (species, population, and community) and metaphors related to the environment (landscape, biome, and ecotone). The developed framework serves as a tool for innovation policymakers, enabling them to make informed decisions by gaining a comprehensive understanding of the seven hierarchical metaphorical layers within innovation ecosystems. Consequently, policymakers should not restrict their governance to the suggestions offered by the prevailing literature on ecosystems. Instead, they should also give equal attention to the various dimensions of innovation ecosystems, including actors, the environment, and the relationships between them, from an ecological perspective.

This paper discusses the application of the Technology Development Envelope (TDE) as a road mapping tool in combination with the Delphi method and the Analytic Hierarchy Process (AHP). The method helps policy and decision-makers in organizations strategically manage emerging technologies by mapping their anticipated development path. The paper focused on creating a Technology Development Envelope (TDE) for artificial intelligence (AI) technologies in the food industry. In the first step, an expert panel identified a list of AI technologies applicable to the food industry. A hierarchical decision-making model, comprising 5 criteria and 24 sub-criteria for technology evaluation, was extracted from the literature and validated using the Delphi method. The Analytic Hierarchy Process (AHP) was used to determine the relative importance of criteria and sub-criteria for evaluating the identified technologies. At this stage, the most important criteria considered were food safety and social and political acceptance of the technology. The expected value of each technology was calculated over different periods, resulting in the development of the Technology Development Evaluation (TDE) for AI technologies in the food industry within a ten-year horizon. The analysis of the future indicates that technologies such as computer vision, robotics, industrial automation, and machine learning will undergo continuous improvements over time and eventually become dominant technologies in the food industry. These advancements can lead to an increase in quality and efficiency within the food industry. Finally, sensitivity analysis was conducted to test the model's sensitivity and explore how policymakers' extreme strategies affect the model's outputs.

Decision-making processes are significantly influenced by a myriad of factors, with data quality emerging as a crucial determinant. Despite widespread awareness of the detrimental effects of poor-quality data on decisions, organizations struggle with persistent challenges because of the sheer volume of data within their systems. Existing literature advocates for providing Data Quality Metadata (DQM) to help decision-makers communicate data quality levels. However, concerns about potential cognitive overload induced by DQM may hinder decision-makers and negatively impact outcomes. To address this concern, we conducted an experimental exploration into the impact of Data Quality Management (DQM) on decision outcomes. Our study aimed to identify specific groups of decision-makers benefiting from DQM and uncover factors influencing its usage. Statistical analyses revealed that decision-makers with a heightened awareness of data quality demonstrated improved Data Quality Management (DQM) utilization, leading to increased decision accuracy. Nevertheless, a trade-off was observed as the efficiency of decision-makers suffered when employing Decision Quality Management (DQM). We propose that the positive impact of incorporating Data Quality Management (DQM) on decision outcomes is contingent on characteristics such as a high level of knowledge about data quality. However, we acknowledge that the inference of this positive impact could be more transparent and thoroughly explained. Our findings caution against a blanket inclusion of Data Quality Management (DQM) in data warehouses, emphasizing the need for tailored investigations into its utility and impact within specific organizational settings.

Evidence-based policymaking stands at the forefront of contemporary governance, where data and evidence have emerged as indispensable allies in shaping effective and informed decisions. This article embarks on a comprehensive exploration of the challenges and critical issues encountered when data assumes the role of evidence in policy formulation. The foundation of this investigation is rooted in the extensive body of literature on evidence-based policy-making. We delve into the scholarly discourse, tracing the evolution of policy formulation from relying on intuition to being guided by empirical insights. As we navigate through this intellectual landscape, the crucial role of data as a catalyst for this transformation becomes apparent. Delving deeper, we will explore the intricacies of data and the rise of big data. Once regarded as mere numbers, data now represents the currency of the information age. Its volume, velocity, and variety characterize it, making it a powerful tool for generating evidence and formulating policies. As we explore its features, we uncover the potential of data to unlock unprecedented insights and inform governance with empirical precision. To conduct a comprehensive examination of the difficulties encountered when utilizing data as evidence in policy-making, we employ a rigorous scoping review methodology. Through meticulous screening, we have identified and analyzed 36 exemplary articles that offer invaluable insights into the multifaceted landscape of data-driven governance. These articles provide a comprehensive overview of the challenges, which can be grouped into three distinct clusters: technical challenges arising from data complexities, legal and privacy dilemmas intertwined with governance, and the formidable issues faced by policymakers. Our discussion unravels the intricate web of challenges, ranging from data quality and integration to confidentiality, ethics, and governance issues. We delve into the intricacies of data access, the fight against bias, and the challenges posed by data volume and complexity. Simultaneously, we explore the complex legal landscape of data ownership, security, sharing, and compliance. The challenges policymakers face in fostering data-driven cultures, navigating resource constraints, and communicating data-driven insights are brought to the forefront. In conclusion, our exploration sheds light on the complex challenges and crucial issues that underlie the use of data as evidence in evidence-based policy-making. This research underscores the transformative power of data in governance and emphasizes the challenges and pressing issues associated with using data as evidence in policymaking.